Fraud Detection using Machine Learning

Fraudulent transactions pose a significant challenge for financial systems, as they can result in major financial losses and reduced trust among users. This project implements a machine learning model to classify transactions as fraudulent or legitimate, focusing on handling class imbalance and ensuring high detection accuracy.

📌 Project Overview

Developed an end-to-end ML pipeline for fraud detection.

Addressed class imbalance using SMOTE and undersampling techniques.

Applied multiple classification algorithms including Logistic Regression, Random Forest, and XGBoost.

Evaluated performance using Precision, Recall, F1-score, Confusion Matrix, and ROC-AUC.

Final model optimized for high recall to minimize undetected fraud cases.

⚙️ Technologies Used

Python

Pandas, NumPy

Scikit-learn

Matplotlib, Seaborn

Imbalanced-learn (SMOTE)

XGBoost

📊 Dataset

The dataset contains both fraudulent and non-fraudulent transactions with a heavy imbalance.

Fraudulent transactions: very rare compared to normal ones

Applied resampling techniques to balance the dataset

Performed feature scaling and preprocessing before training

(If dataset is from Kaggle, add link here)

🚀 How to Run

Clone the repository

git clone https://github.com/your-username/fraud-detection-ml.git cd fraud-detection-ml

Install dependencies

pip install -r requirements.txt

Run the notebook / script

jupyter notebook Fraud_Detection.ipynb

📈 Results

High recall score ensuring minimal fraud goes undetected

Balanced trade-off between precision and F1-score

Visualization of performance with ROC curve and confusion matrix

🔮 Future Improvements

Deploy as a REST API or integrate into financial systems

Try deep learning models for improved accuracy

Implement real-time fraud detection pipeline

🤝 Contributing

Contributions are welcome! Please fork this repo, make changes, and submit a pull request.

📜 License

This project is licensed under the MIT License.